Note
10,000 iterations have been passed so far
This is a Fashion-visualizer based on finetuned detectron2 model with iMaterialist FGVC7 dataset.
- Detectron2 is an open-source object detection and segmentation framework developed by Meta. It provides a flexible and extensible platform for building and training state-of-the-art models for various computer vision tasks, including object detection, instance segmentation, and semantic segmentation.
- iMaterialist FGVC7 is a large-scale fashion dataset created for the Fashion and Apparel Classification Challenge. It consists of a diverse collection of fashion images sourced from various e-commerce platforms and annotated with fine-grained labels.
First you need to install detectron2 package, you can follow detectron2 official installation or the code down below copy from installation page.
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)
# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2
# On macOS, you may need to prepend the above commands with a few environment variables:
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ...
Then use pip install -r requirements.txt to install package from requirements.txt file.
Note
Because of github LFS bandwidth limit, so I put the .pth files on my google-drive where you can download and put into config folder straightly. 2024/5/20 Download link
Using CLI to interact with visualizer.
Command line interface
usage: Fashion-visualizer.py [-h] [--pt PT] [--th TH] [--wt WT] [--md MD]
--- Fashion-visualizer ---
options:
-h, --help show this help message and exit
--pt PT Input image path
--th TH Prediction thershold
--wt WT Model weight file(V1)
--md MD Metadata json path
Set predict image path.
python Fashion-visualizer.py --pt /YOUR_IMAGE_PATH
Adjust the threshold for the prediction, which defaults to 0.8, also can change the weight file of the model.
python Fashion-visualizer.py --pt /YOUR_IMAGE_PATH --th 0.7 --wt V2
In the future, models with more iterations will be released, so you can change the model weight file for different use cases.
Model list
✔️ modelV1 - 1,000 passed iteration
✔️ modelV2 - 5,000 passed iteration
✔️ modelV3 - 10,000 passed iteration
✔️ modelV4 - 20,000 passed iteration
MIT License
Copyright (c) 2024 Jsnn
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